An Efficient Approach to Semantic Segmentation

Abstract

We consider the problem of semantic segmentation, i.e. assigning each pixel in an image to a set of pre-defined semantic object categories. State-of-the-art semantic segmentation algorithms typically consist of three components: a local appearance model, a local consistency model and a global consistency model. These three components are generally integrated into a unified probabilistic framework. While it enables at training time a joint estimation of the model parameters and while it ensures at test time a globally consistent labeling of the pixels, it also comes at a high computational cost. We propose a simple approach to semantic segmentation where the three components are decoupled (this journal submission is an extended version of the following conference paper: G. Csurka and F. Perronnin, "A simple high performance approach to semantic segmentation", BMVC, 2008). For the local appearance model, we make use of the Fisher kernel. While this framework was shown to lead to high accuracy for image classification, to our best knowledge this is its first application to the segmentation problem. The semantic segmentation process is then guided by a low-level segmentation which enforces local consistency. Finally, to enforce image-level consistency we use global image classifiers: if an image as a whole is unlikely to contain an object class, then the corresponding class is not considered in the segmentation pipeline. The decoupling of the components makes our system very efficient both at training and test time. An efficient training enables to estimate the model parameters on large quantities of data. Especially, we explain how our system can leverage weakly labeled data, i.e. images for which we do not have pixel-level labels but either object bounding boxes or even only image-level labels. We believe that an important contribution of this paper is to show that even a simple decoupled system can provide state-of-the-art performance on the PASCAL VOC 2007, PASCAL VOC 2008 and MSRC 21 datasets.